JAQM Volume 9, Issue 1 - March 30, 2014

Contents

As the complexity of reliability systems grew, they became increasingly dependent on the human factor. Major disasters like the one at Three Mile Island, the Challenger ship, Chernobyl, nuclear power plant accidents, aviation, industrial disasters, etc. have been attributed to the human factors. Hence the need to study the reliability of the human factor as a distinct element of the reliability of the technical and economic systems. After some conceptual clarifications, we are investigating the state of knowledge in the field of human reliability. The literature provides as usual solutions, description and calculation errors, mainly, Goel-Okumato models of class time-domain and time-domain class, where the most notorious is the model Jelinsnski-Moranda, both inspired by the reliability of the software. The study presented in this material, the suitability of a statistical model is able to capture, process, and faithfully carry out human error in "man-machine" system. The application is based on the data recorded from the testing human operators in the management of complex technical systems. Through generalization, the process can be extended to other complex systems such as "man-machine”.

The paper discusses the practical implementations of using the support vector machine (SVM) algorithm for imbalance data to predict the stock performances in the Indonesian stock market. SVM algorithm for imbalance data was used to model various financial ratios as independent variables to investigate indicators that significantly affect the stock’s performance of large market capitalization companies which were actively traded over the last three-year periods. The model selections, namely the imbalance and the balance SVM model with dummy variables representing the appropriate weights were carried out using 10-fold cross validation methods integrated with a grid search procedure for parameter optimization. The study identified and examined six financial ratios commonly used by the stock analysts without considering macro economic variables was able to classify the performances of the companies into two categories “good” or “poor” based on the prices proportion of two consecutive periods. The result suggested that the proposed method yield competitive performance in terms of prediction accuracy level as compared with its competitors.

Characteristics of mobile applications in terms of development, usage and investment process are highlighted; analysed from the point of view of the investor, target group and return on investment. Mobile applications are designed to record user behaviour and databases assembled during this process represents raw material for the reengineering process. Recording user behaviour is showcased in the context on an actual application. Indicator of user behaviour are defined and submitted for debate. Indicators are validated by using data from a real life application. A model that facilitates the user oriented reengineering process is build. An efficient way of determining the optimal period for triggering the reengineering process is submitted.

This paper sheds light on the complex interrelationships between history and mathematical developments by examining Archimedes' Cattle Problem. With modern computing capacities, what seemed like an almost impossible computational problem in Archimedes day is no longer such a seemingly intractable problem. This paper demonstrates the formulation of Archimedes’ Cattle Problem through an Excel spreadsheet utilizing an Excel add-in optimizing tool. After providing the solution to Archimedes' Cattle Problem, the paper then argues that intractable mathematical solutions of bygone eras become facilitated over time by the use of new instruments. The paper concludes by noting that in certain cases it is difficult to separate the virtues of the instrument from those of the observer. Had modern computing capacity been available in Archimedes day, the solution to his cattle problem would have been readily at hand.